Advanced Structural Analysis using the DNN-LALM Method for Bridge Damage Detection

The reliability, functionality, and safety of concrete bridges are reliant on the prompt and accurate detection of surface degradation. New DL-based visual inspection algorithms can spot a wide variety of structural defects, thanks to recent developments in convolutional neural networks. Nevertheless, when it comes to sophisticated autonomous inspection systems, the majority of deep learning methods depend on a two-stage procedure and use proposal-driven detectors trained on simpler image data. This could potentially restrict their applicability. It is necessary to successively execute preprocessing, model training, and feature extraction in order to improve bridge damage detection. Image enhancement, noise processing, and greying are all part of the preprocessing phase. To extract useful insights from complicated datasets, Principal Component Analysis (PCA) is employed for feature extraction. In order to train the DNN-LALM model, feature extraction is essential for correct structural feature identification. With a detection accuracy of 96.71%, the suggested method surpasses two cutting-edge algorithms: DNN and LALM. In order to ensure the safety and longevity of concrete bridges, these findings contribute to the evolution of smart inspection technologies by demonstrating the usefulness of a structured deep learning approach in bridge damage identification.